Predictive integrity analysis

ABSTRACT

A system includes one or more tools, sensors, or both configured to obtain data related to the one or more pipelines, wherein the data is ultrasonic data, electromagnetic data, or both, and a cloud-based computing system including at least one processor that receives the data from the one or more tools, sensors, or both, performs analysis to generate a virtual structural model of the one or more pipelines based on the data, determines one or more states of the one or more pipelines using the virtual structural model and determines whether to take one or more actions when the one or more states indicate that the one or more pipelines violate a threshold operation boundary.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/870,304 filed on Jan. 12, 2018, entitled “Predictive IntegrityAnalysis,” which claims priority to U.S. Provisional Patent ApplicationNo. 62/474,460, filed Mar. 21, 2017, entitled “Predictive IntegrityAnalysis,” which are hereby incorporated by reference in theirentireties.

BACKGROUND

The subject matter disclosed herein relates to integrity analysis, andmore particularly, to a predictive integrity virtual analysis ofstructural and operational conditions of one or more pipelines.

After certain components, such as pipelines, are installed andcommissioned, it may be difficult to ascertain the condition of thecomponents over time as fit for operational service and the like. Assuch, improved systems and methods for monitoring the integrity of thesecomponents over time may be desirable.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the original claims aresummarized below. These embodiments are not intended to limit the scopeof the claims, but rather these embodiments are intended only to providea brief summary of possible forms of the disclosed embodiments. Indeed,the claims may encompass a variety of forms that may be similar to ordifferent from the embodiments set forth below.

In one embodiment, a system includes one or more tools, sensors, or bothconfigured to obtain data related to one or more pipelines, wherein thedata comprises ultrasonic data, electromagnetic data, or both, and acloud-based computing system comprising at least one processor toreceive the data from the one or more tools, sensors, or both, performan analysis to generate a virtual structural model of the one or morepipelines based on the data, determine one or more states of the one ormore pipelines using the virtual structural model, and determine whetherto take one or more actions when the one or more states indicate thatthe one or more pipelines violate a threshold operation boundary.

In an embodiment, a device includes an input configured to receivemeasured data indicative of a physical characteristic or an operationalcharacteristic of a pipeline, and a processor coupled to the input andconfigured to receive the data from the input, perform an analysis togenerate a virtual structural model of the pipeline based on the data,determine one or more states of the of the pipeline using the virtualstructural model, and determine whether to take one or more actions whenthe one or more states indicate that the pipeline violates a thresholdoperation boundary.

In an embodiment a non-transitory tangible computer-readable mediumincludes computer executable instructions configured to receive measureddata indicative of a physical characteristic or an operationalcharacteristic of a pipeline, perform an analysis to generate a virtualstructural model of the pipeline based on the data, determine one ormore states of the of the pipeline using the virtual structural model,and determine whether to take one or more actions when the one or morestates indicate that the pipeline violates a threshold operationboundary.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the disclosedembodiments will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 an illustration of a system for performing predictive integrityanalysis, in accordance with an embodiment of the present disclosure;

FIG. 2 is a flow diagram of a process suitable for performing predictiveintegrity analysis, in accordance with an embodiment of the presentdisclosure;

FIG. 3 is an illustration of a block diagram of various components ofthe system of FIG. 1, in accordance with an embodiment of the presentdisclosure; and

FIG. 4 is an illustration of a visualization of a portion of a pipelineat an element level, in accordance with an embodiment of the presentdisclosure.

DETAILED DESCRIPTION

One or more specific embodiments will be described below.

Pipeline integrity can relate to the safety and/or operations of thepipeline through monitoring, assessment, and/or prevention of any issuesthat may affect the structural fit for service of a pipeline totransport its products (e.g., hydrocarbons, natural gas, oil, etc.) atdesired operational levels (e.g. mass-flow, pressures). Various inlineinspection (ILI) tools and sensors may obtain data related to structuraland physical characteristics of certain components, such as pipelines.For example, sensors may acquire corrosion data that indicates whether awall of a pipeline has thinned, and other sensors may acquire crackingdata that indicates whether the wall has split apart. Other sensor typesmay capture geometric information such as the cross-sectional shapesand/or centerline direction of the pipe. In addition, other sensor typesmay capture information related to the material properties of the pipematerial. Still other sensors types may capture environmental propertiesof the pipeline operation as measured around the tool, such as localpressure, temperature, product density, and/or composition at any givenpoint in the pipeline. The available datasets may also include thelocation and localized geographic information with reference to thepipelines as known for the pipeline from the inline inspection tools orabove-ground geographic surveys. The available datasets may be examinedto identify correlations between the datasets; however, this process mayprove to be complex and time consuming.

Embodiments of the present disclosure generally relate to systems andmethods for access and assessment of pipeline integrity and reliabilityusing a virtual model generated in a distributed cloud-computingenvironment (e.g., a cloud-based computing system) from the availabledata sources. In embodiments, the systems and methods described hereinmay provide rapid and/or detailed access and assessment. It should benoted that one or more of one pipeline, many pipelines, a network ofpipelines may be modeled. For example, a virtual set of pipelinenetworks (e.g., series of networks of pipelines) may be simulated andvisualized using the disclosed techniques. Continuous and segmented datasets regarding the state of the pipelines, such as inline inspection(MI), may be loaded, correlated, and used to record historical stateinformation regarding a structural frame of the pipelines and/or currentstate information regarding the state of the structural frame. Thevirtual model of such state of the structural frame can be assessedagainst known engineering codes and practices, as well as advancedcomputational analysis techniques like FEA and structural designmethods. This information may also be processed with change/growthmethods to determine a variety of predicted future state(s) at differentfuture time periods, to determine reliability characteristics of thestructural frame at those time periods and to determine optimalpreventative maintenance methods and timing as well as pipeline downtimefor such activities (e.g., replacement of pipe, patchwork, decrease loadconditions). As a result, the future reliability and predictivemaintenance operations can be modeled and available for use in timelymanner.

In some embodiments, the cloud-based computing system may correlate,manage, and/or compute structural information that is received frommultiple ILI and other inspection/structural descriptive data setsobtained via ILI tools and/or sensors to form a virtual structure. Theprocessing may be handled and managed in the cloud-based computingsystem by using a parallel high performance computing framework with anumber of computing systems working together. High performance computingpower can enable rapid computation of statistics, probabilisticreliability assessments, and quasi-real time structural evaluation at adetailed elemental level (e.g., anomalies may be identified andevaluated via preset industry assessment codes and/or via finite elementanalysis (FEA)/multi-physics engine or similar computational methods forstructural assessment). Using a cloud-based computing system can alsoenable remote access functionality for select users internally to thegiven organization, and/or for select external stakeholders as wellregarding such predictive analyses.

Using the disclosed embodiments may enable data traceability forengineering decisions related to the data, predicting integrity ofcurrent state/reliability and future state reliability for one or morepipelines, and/or data organization and management in a cloud-basedcomputing system. Further, shared resources and parallel computingenabled by the cloud-based computing system may enable rapidcomputations and reduced complexity and errors in time-lag/manual datahandling transition within computation of integrity and engineeringassessments.

To that end, the disclosed embodiments may remove or reduce manualhandling of data, correlation, and/or assessment. In some embodiments,the cloud-based computing system may use existing industry engineeringcodes and/or computational methods such as finite element analysis (FEA)to establish structural integrity predictions in a thorough (e.g., fullpipeline) and timely manner.

In addition, “combined” or “multiple” threat cases may be considered bythe nature of the virtual model structure within the cloud-basedcomputing system. For example, the virtual model may illustrate that abending strain area of a pipeline overlaps with a corrosion area, whichforms a different structural situation than each of the individualthreats alone. Corrosion and crack anomalies may be found and assessedin the same localized region, dented/deformed regions of the pipe alsohaving corrosion or cracks in the pipe wall may be modeled for fatiguevia FEA, and the like. Such modeling can include operational parameterssuch as product pressure, temperature as taken from the sensor datasources or as assumed values for the given location. Future statepredictions may be achieved with the use of a baseline current state andchange/growth models to each point of the virtual pipeline of eachdegrading mechanisms (e.g., crack growth, corrosion growth, dentfatigue, geotechnical movement).

With the foregoing in mind, FIG. 1 is an illustration of a system 10 forperforming predictive integrity analysis, in accordance with anembodiment of the present disclosure. The system 10 includes one or morepipelines 12. As mentioned above, the pipelines 12 may include a singlepipeline, a network of pipelines, or a series of networks of pipelines.The pipelines 12 may transport product (e.g., hydrocarbons, oil, naturalgas, etc.) and may be disposed below and/or above ground. Determiningthe mechanical state of the pipelines 12 efficiently and in quasi-realtime may enable the reduction of lost operational time of thepipeline(s) as well as optimization of the identification of the timing,type, and instructions related to repairs, among other things. Thus,some embodiments can use a cloud-based computing system 14 that can usea virtual model of the pipelines 12 to enable the assessment ofhistoric, current, and/or future structural and operational states ofthe pipelines. The cloud-based computing system 14 may determine whetherto take one or more actions based upon the assessment (e.g., performmaintenance, replace a part, schedule maintenance, etc.). For example,the cloud-based computing system 14 may alter (e.g., reduce) operatingparameters of the system 10 so as to reduce stress on an assessedportion of the pipeline(s) 12 so as to extend an amount of time thesystem 10 can operate before maintenance and/or part replacement occurs.In this manner, the cloud-based computing system 14 may operate toreduce lost operational time of the pipeline(s) 12 during periods ofhigh demand and instead adjust maintenance and/or part replacement timeperiods to periods of lowered activity in the system 10. Likewise, forexample, the cloud-based computing system 14 may alter (e.g., increaseor decrease) operating parameters of the system 10 so as to increase orreduce stress on an assessed portion of the pipeline(s) 12 so as tocause portions of the system 14 to have scheduled maintenance and/orpart replacement at similar times (e.g., to match maintenance and/orpart replacement schedules for the pipeline(s) 12 so that the frequencyof maintenance and/or part replacement of the pipeline(s) 12 isreduced). A user or operator may thereafter carry out the one or moreactions, such as by performing maintenance, replacing a part,scheduling/performing maintenance, etc.

To enable modeling the virtual pipelines, data from numerous sensors 16and/or inline inspection (ILI) tools 18 may be used throughout thepipelines 12 to obtain data related to the pipelines 12. The sensors 16and/or ILI tools 18 may obtain magnetic, ultrasonic, radiographic and/orelectromagnetic data regarding its surrounding environment, thecondition of the pipelines 12, and the like. The sensors 16 and/or ILItools 18 may transmit that data to the cloud-based computing system 14.The signal data from ILI tools 18 and sensors 16 may be analyzed andprocessed on a computing device 30, or in the cloud-based computingsystem 14 on server 20 using processor 26, memory 27, or both viacommunication component 28. Some signal ILI navigational data may beprocessed to generate a continuous pipeline centerline in globalpositioning system (GPS) coordinates to enable the cloud-based computingsystem 14 to map the measurements with various locations in thepipelines 12. In some instances, the ILI tools 18 may traverse thepipelines 12 to obtain images of the pipelines 12. The ILI tools 18 mayinclude an inertial or positioning sensor probe that may or may not beattached to a wire. The data obtained by the ILI tools 18 and/or thesensors 16 may provide indications of one or more of volumetric wallloss by location, orientation of the pipeline 12 (e.g., length, width,height), cracking, pressure, flow, geometry of the pipeline (e.g., todetermine whether external force has hit the pipeline 12), a centerline,weld anomalies, bending strains, transition/fittings/facility, otherstrain loading, material properties, and the like.

The data obtained via the ILI tools 18 and sensors 16 may be received byone or more servers 20 of the cloud-based computing system 14 and storedin one or more memories 22 of the servers 20 or in one or more databases24 included in the cloud-based computing system 14 that are external tothe servers 20. The servers 20 may be communicatively coupled to eachother and may distribute various tasks between each other to perform thetasks more efficiently. The servers 20 may also include one or moreprocessors 26 and a communication component 28. The communicationcomponent 28 may be a wireless or wired communication component that mayfacilitate communication between the cloud-based computing system 14,the ILI tools 18, the sensors 16, and/or a computing device 30.

The processor 26 may be any type of computer processor or microprocessorcapable of executing computer-executable code. The processor 26 may alsoinclude multiple processors that may perform the operations describedbelow. The memory 22 may be any suitable article(s) of manufacture thatcan serve as non-transitory media to store processor-executable code,data, analysis of the data, or the like. These articles of manufacturemay represent computer-readable media (e.g., any suitable form of memoryor storage) that may store the processor-executable code used by theprocessor 26 to perform the presently disclosed techniques. Generally,the processor 26 may execute computer instructions that virtually modelpipelines 12 based on a multitude of data received from the ILI tools 18and/or the sensors 16 using various techniques (e.g., finite elementanalysis). In some embodiments, due to the distributed nature of theservers 20 in the cloud-based computing system 14, the shared resourcesof the servers 20 can enable parallel processing of the modeling toenable quasi-real time feedback. For example, each server 20 may beresponsible for processing a different portion of the model atsubstantially the same time and a single server 20, that combines theresults of the model and outputs the results to the computing device 30,may collect the results. In this way, no one server 20 is inundated withthe computationally expensive task of virtually modeling the entirepipeline 12 system and the processing time may be reduced.

The servers 20 may receive data from one or more of the ILI tools 18and/or sensors 16 and generate a virtual pipeline by using variousmodeling techniques (e.g., mathematic, physics-based). The servers 20may transform the received data into a different format that can be usedto create virtual pipeline. Using the modeled virtual pipelines 12, theservers 20 may evaluate the pipeline state (e.g., past, current, future)by using finite element analysis. The servers 20 may predict futuretimeframes of when certain conditions may occur and subsequently whenadditional actions may be taken, such as repair, pipelinedepressurization, or shutdown. In some instances, the servers 20 maycorrelate numerous identified issues, such as corrosion, cracks, complexfeatures, geometry, weld anomalies, bending strain, stress, materialproperties, and the like within a full structural analyses, as opposedto analyzing each issue individually in a simpler but potentiallylimited engineering calculation. Using the current data, the servers 20may extrapolate future growth or future states of when the pipelines 12may stop operating within a desired range. The desired ranges may bepredetermined ranges for any pipeline 12 and the desired ranges mayrelate to product throughput (flow), pressure, and the like. The servers20 may perform the computations of various processes described below innear real time either on demand from users and/or automatically based oncertain data change triggers, as received from data updates via sensors16, ILI tools 18, and/or data sets on record in database 24 and/ormemory 27. Interested stakeholders and users may access the results viacomputing device 30.

The databases 24 may be related to various aspects of the pipelines 12.For example, the databases 24 may include information regarding variousregulations related to how the pipelines 12 should be maintained.Additionally, the regulations may be related to how maintenanceoperations should be documented by the user of the computing device 30.The databases 24 may also include data related to warranty informationfor the pipelines 12, service contact information related to thepipelines 12, and other information that may be useful to an operator ofthe pipelines 12. Further, the databases 24 and/or the memory 22 maystore historical sensor and/or ILI data, as well as historical statedata related to the pipelines 12 determined by the processors 26.

The computing device 30 may store an application that provides agraphical user interface (GUI) that displays the visualization of themodeled pipelines 12, as well as any predictions and/or actions (e.g.,maintenance, repair, replacement, etc.) to be taken. That is, theapplication may not perform any computationally intensive processing.Instead, in some embodiments, the application may function as afront-end display of data and results of the integrity predictingmodeling performed by the cloud-based computing system 14. For example,in a client-server architecture, a website may be accessed via a browseron the computing device 30 and the website may function as a thin-clientin that it just displays information provided by the cloud-basedcomputing system 14 without actually performing any modeling.

Although the components described above have been discussed with regardto the servers 20 of the cloud-based computing system 14, it should benoted that similar components may make up the computing device 30.Further, it should be noted that the listed components are provided asexample components and the embodiments described herein are not to belimited to the components described with reference to FIG. 1.

FIG. 2 is a flow diagram of a process 40 suitable for performingpredictive integrity virtual analysis, in accordance with an embodimentof the present disclosure. Although the following description of theprocess 40 is described with reference to the processor 26 of one ormore servers 20 of the cloud-based computing system 14, it should benoted that the process 40 may be performed by one or more otherprocessors disposed on other devices that may be capable ofcommunicating with the cloud-based computing system 14, such as thecomputing device 30, or other components associated with the system 10.Additionally, although the following process 40 describes a number ofoperations that may be performed, it should be noted that the process 40may be performed in a variety of suitable orders and all of theoperations may not be performed. It should be appreciated that theprocess 40 may be distributed between the servers 20 of the cloud-basedcomputing system 14, distributed between local devices and the servers20, individually, or any combination of the devices.

Referring now to the process 40, the processor 26 may receive (block 42)data related to the one or more pipelines 12 from the ILI tools 18and/or the sensors 16. In some instances, the amount of data may belarge due to the frequency of measurements and the sheer size of thepipeline networks being measured. The data may relate to materialproperties obtained from inspection data or other physical operationaldata available for a region of interest. The data may include ultrasonicand/or electromagnetic measurements, among other things. In someembodiments, the data may be stored in the one or more databases 24 inthe cloud-based computing system 14. Additionally or alternatively, thedata may be stored in the memories 22 of each or in some of the servers20 in the cloud-based computing system 14. In this way, the cloud-basedcomputing system 14 enables storing the ILI data and/or sensor data in asingle location. Further, historical data may be maintained in thedatabases 24 and/or the memories 22.

In some embodiments, the data may be indicative of certain issues, suchas corrosion (e.g., metal loss resulting in wall thinning), cracking(e.g., pipeline split open due too much pressure), pipeline geometry(e.g., abnormal radius), stress loading (e.g., earthquake, flood,excavation, construction in area), pressure within pipelines 12, flow,weld anomalies, material properties, and the like. In some embodiments,the data may be formatted to enable finite element analysis. Forexample, the data may be formatted using a convention related to a “boxlisting” or “location listing.” The box listing or location listing mayinclude a physical location (e.g., geographic, such as a distance from areference number) of the box in the pipelines 12, an identifier, and/orcertain details of the box (e.g., orientation (length, width, height)),that define the physical feature attributes represented by that box. Thebox listing or location listing may be represented concurrently in aspreadsheet and the listings may enable an operator to find the box onthe pipeline 12 to repair it, replace it, or the like as a parallel dataset to the results of the more detailed FEA analyses. That is, the boxlisting or location may enable virtually representing the pipeline 12 inelemental structure using a computer-aided design (CAD) image thatrepresents defects or loading conditions on the pipeline 12 to enableadvanced assessments, as described below.

The processor 26 may also perform (block 44) analysis to model one ormore physical states of the one or more pipelines 12 based on the data.In particular, in some embodiments, the processor 26 may use the dataformatted as described above to perform finite element analysis orsimilar computational structural analyses. For example, the processor 26may correlate the element level data (e.g., generated from corrosion bylocation, cracking by location, stress loading by location, etc.) asrepresented from data sources of inline inspection 18, sensors 16, tomap properties of the pipelines 12 for each elemental location togenerate a virtual model of the entire pipeline. In some embodiments,the processor 26 may use modeling (e.g., set of mathematical equations,physics-based) to generate the virtual structural model of the pipelines12. The virtual structural model may include a visualization of thephysical pipelines 12. The finite element analysis may be performed atthe element level to enable a more detailed assessment of correlatedissues (e.g., corrosion, cracking, stress loading, flow, pressure,etc.).

The processor 26 may use the virtual structural model to determinehistoric states of the pipelines 12, current states of the pipelines 12,and/or predicted future states (e.g., with the use of change/growthmethods). For example, performing finite element analysis on theformatted data may enable predicting how the pipelines 12 react toreal-world forces, corrosion, cracking, pressure, vibration, heat, fluidflow, and other physical effects. In addition, the finite elementanalysis may be subdivided in smaller computations (e.g., algebraicequations, partial differential equations, etc.) between servers 20 soportions of the pipelines 12 may be modeled separately. Breaking theproblem domain into smaller portions may enable faster compute time andmore accurate representation of complex geometry and physical state ofthe pipelines 12.

Further, in some embodiments, the various current states may be storedas historic states in the databases 24 and/or the memories 22. Theprocessor 26 may generate overlapping representations of the historicstates of the pipelines 12 with the current states of the pipelines 12to enable visualizing the change in the structural composition of thepipelines 12 over time. In some embodiments, the historical state datamay be maintained for extended periods of time, such as decades.

The processor 26 may determine the pipeline state and any future statesto determine or predict when the pipelines 12 or portions of thepipelines 12 stop operating within a desired threshold boundary or range(e.g., detected level of corrosion). For example, if the ILI data orsensor data indicates that a threshold boundary is violated, then theprocessor 26 may perform (block 46) one or more actions based on the oneor more states of the one or more pipelines 12. Additionally, afterperforming finite element analysis and the processor 26 predicting thatthe flow of product in any portion of the pipeline 12 may fall below thethreshold boundary, the processor 26 may also perform (block 46) the oneor more actions based on the one or more states of the one or morepipelines 12. The assessment may change when conditions of the pipelineschange to enable detailed analysis, and may be recalculated using thecloud-based computing system 14.

The actions may include displaying an alert on the graphic userinterface (GUI) of an application installed on the computing device 30.The alert may highlight a portion of the pipeline 12 where the violationof the threshold boundary is predicted or detected, or may include agraphic (e.g., a flashing exclamation mark) that is overlaid on theportion of the pipeline 12 where the violation of the threshold boundaryis predicted or detected. The alert may provide details as to the issuesrelated to the portion of the pipelines 12 and a timeframe for when anyundesirable condition (e.g., cracking or bending of the pipelines 12that causes the reduced flow) may occur. Additionally, the actions mayinclude scheduling maintenance, repair, and/or replacement of portionsof the pipelines 12. In some embodiments, the scheduling may beperformed via the GUI of the application executing on the computingdevice 30. Further, in some embodiments, the actions may includestopping the flow of product in the pipelines 12 when the current stateor predicted state indicates a sufficiently severe condition.

FIG. 3 is an illustration of a block diagram of various components ofthe system 10 of FIG. 1, in accordance with an embodiment of the presentdisclosure. As depicted, various ILI tools 18 may include electronicsand/or mechanics and vehicles that are used to obtain ILI data. Forexample, a probe may be attached to a wire that is dispensed through thepipeline networks to obtain ultrasonic and/or electromagnetic dataindicative of properties of the pipelines 12. Also, various sensors 16(e.g., pressure, flow, vibration, thermal, etc.) may be used as part ofthe ILI tool 18 or as individual externally configured sensors on thepipelines 12. The data may be sent to the cloud-based computing system14 that performs data analysis and synthesis (block 50). Data analysisand synthesis may include performing multiple sensor 16 technology datainterpretation and synthesis (block 52), which uses ILI 18 technologyper threat interpretation (block 54).

During block 52, the processor 26 may also perform (block 56)multiple-threat alignment and IE assessment. That is, the processor 26may correlate the various issues, such as corrosion, cracking, strainloading, and the like, at the element level for each section of thepipelines 12 to build the virtual structural model used for determiningthe states of the pipelines 12. The processor 26 may perform finiteelement analysis to predict whether the pipeline 12 future stateviolates a threshold operating boundary or range. The multiple-threatalignment and assessment may enable and streamline systematiccombinational threat assessment in a highly detailed way using elementlevel data in a finite element model. The sensor data and/or the statedata (e.g., historic, current, and/or future) may be stored in theshared and compatible data repository (e.g., databases 24). The datathat is stored in the databases 24 may be monitored (block 58) to enabletaking one or more actions when a desired operating threshold range orboundary is violated. In some embodiments, the processor 26 mayperiodically clean (block 60) the data by purging certain records.

The database 24 may maintain data for an extended period of time (e.g.,decades) to enable visualizing the entire virtual history of the pipe byoverlapping the states on a virtual structural model representation ofthe pipelines 12. The historical visualization may illustrate thehistorical states of the pipeline 12 to show the transformation of thepipeline 12 over time. The database 24 may also store other vendor data(block 62) and/or third party/dig verification data (block 64).

The processor 26 may perform reporting (block 66) by outputting theresults of the predictive integrity virtual analysis that used thevirtual structural model to determine states to an application installedon the computing device 30 or a website hosted by one of the servers 20.The processor 26 may perform combined or standalone analysis reporting(block 68). Combined analysis reporting may refer to integrity virtualanalysis that combines and correlates the various threats (e.g.,corrosion, cracking, and stress loading) in the virtual structural modelto make predictions using finite element analysis, or the like. Combinedanalysis reporting may enable providing each threat related to aparticular joint of the pipelines 12. Standalone analysis reporting mayrefer to just analyzing the integrity of the pipelines 12 based on asingle threat (e.g., corrosion) selected by an operator. Visualizationand interpretation prioritization modules (block 70) may be used by theprocessor 26 to enhance interfacing and interaction with the data forthe operators. That is, the modules may draw the virtual structuralmodel of the pipelines 12, the alerts on the virtual structural model,arrange various information on the GUI, and the like for example, foreasy viewing by the operator.

The processor 26 may deliver (block 72) the results to various personnelin an organization. For example, the results may be delivered to a salesteam, a project manager with oversight of the pipelines 12, technical orproject managers as stakeholders of the results and/or a pipelineoperator's engineering team. Each of the personnel may have installedthe application on their computing device 30 or have secure access tothe website. That is, role-based security may be enabled where justpersonnel with proper role and credentials are allowed to see theresults of the predictive integrity virtual analysis. In someembodiments, the application that is used on the computing device 30 maybe downloaded from a software distribution platform, such as anapplication store, that authorizes the personnel prior to downloadingthe application. In some embodiments, the personnel provide theircredentials at a login screen, and the website authenticates the userprior to providing access to the results.

An integrity management (IM) plan (block 74) for the pipelines 12 may begenerated based on the results of the predictive integrity analysis. TheIM plan may consider maximizing throughput of product in the pipelines12, minimizing interruption to operation, and performing theseobjectives cost effectively. The IM plan may provide instructions to theoperator to perform maintenance on certain portions of the pipelines 12according to certain timing based on the results from the predictiveintegrity virtual analysis. For example, the virtual structural model ofthe pipeline 12 may enable the operator to visualize that a portion ofthe pipe includes an abnormal radius and general metal loss (e.g., wallthinning), which affects the product carrying capacity of the pipeline12 under full operating conditions. Based on the correlated threatinformation, the IM plan may include replacing that portion of thepipeline 12 when product delivery is expected to be reduced to minimizean overall interruption of pipeline operation. In addition, a riskprotocol and historic IM data (block 76) may include a procedure to dealwith pipeline condition that is included in the threat assessment.

An illustration of a visualization of a virtual structural model 80 of apipeline 12 is depicted in FIG. 4, in accordance with an embodiment ofthe present disclosure. The virtual structural model 80 may be generatedby the processors 26 of the various servers 20 in the cloud-basedcomputing system 14. As depicted, the virtual structural model 80depicts a representation of the actual structure of the pipeline 12. Theoperator may select a portion 82 of the virtual structural model 80 toreceive more information of that portion 82 at a zoomed-in view. Asdepicted, the portion 82 in the zoomed-in view may provide the operatorwith various information, such as centerline (X, Y, Z), or GPScoordinates (N, E, H), azimuth orientation angle of the pipeline 12,transverse/circumferential, radial, and axial angles, among others. Thecenterline may be local earth frame reference. The pipeline 12 radiusmay be given by caliper (e.g., dent deflection as function of azimuth,etc.).

The operator may drill down further into a more granular portion 84 ofthe pipeline 12 to see information visualized using finite elementanalysis at the element level. The granular portion 84 at the elementlevel provides a three-dimensional (3D) spatial grid representation ofthe data provided in a spreadsheet 86 that is based on the data 88formatted in the databases 24 and/or the memories 22. The granularportion 84 may provide an actual physical representation of the variousthreats indicated by the data. That is, the granular portion 84 canreconstruct the threats and the physical structure of the pipelines 12using the data that is formatted for finite element analysis (FEA) orcomputer-aided design (CAD). In some embodiments, the FEA and/or CADformatted data may include material properties assigned.

As may be appreciated, the 3D spatial grid representations may be usedto enable visually representing cracks 90 (discontinuity betweenelements). For example, the cracks may have a length and depth but nowidth and the crack may be localized by distance and azimuth. The 3Dspatial grid representation may also visualize welds and other featuresthat can be represented accordingly with geometric and noted materialchanges. Also, the 3D spatial grid representation may visualize abnormalradius (e.g., within dent/deformation is function of distance azimuth).Also, the 3D spatial grid representation may represent mid-wall features94, as well as metal loss 96 where the elements are not comprised ofsteel but of air (in 3D). The pipeline 12 will contain physical fittingssuch as branches (e.g., off takes of line to other pipelines), stopples,taps and operational equipment such as valves, pumps, compressors, tankswhich may be included in the virtual model to varying levels of preciserepresentation in a virtual model according to the information from thedata sources and basic user choice. Default representations of the mainpipeline, fittings and equipment could be preselected from a presetlibrary of representations for that type of item for use in any virtualmodel analyses.

Technical effects include providing systems and methods for predictiveintegrity virtual analysis of pipelines 12 to enhance life of thepipelines 12, reduce costs of servicing and repair, and so forth. Thetechniques disclosed herein may remove and/or reduce manual handling ofdata, correlation, and assessment. The techniques may use industryengineering codes and/or computational methods, such as FEA, toestablish structural integrity predictions in a very thorough (fullpipeline) and timely manner (using high performance computing providedby the cloud-based computing system 14). Combined threats may beconsidered by the nature of the virtual structural model generated. Forexample, the virtual structural model may illustrate that bending strainarea overlaps with a corrosion area and/or crack anomalies are presentin the same area. Further, dented and/or deformed regions with corrosionor cracks may be modeled for fatigue via FEA. Future state predictionsmay be enabled with the use of baseline current states and addition ofchange/growth models to each point of the virtual pipeline for eachdegrading mechanism (e.g., crack growth, corrosion growth, dent fatigue,geotechnical movement). Further, visualization of the entire history ofthe states of the pipelines 12 may be enabled by overlapping the statesin the virtual structural model.

This written description uses examples to disclose embodiments,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the embodiments is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

Other embodiments are within the scope and spirit of the disclosedsubject matter.

In an effort to provide a concise description of these embodiments, allfeatures of an actual implementation may not be described in thespecification. In the development of any such actual implementation, asin any engineering or design project, numerous implementation-specificdecisions must be made to achieve the developers' specific goals, suchas compliance with system-related and business-related constraints orpreferences, which may vary from one implementation to another.Moreover, it should be appreciated that such a development effort mightbe complex and time consuming, but could nevertheless be a routineundertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments, the articles “a,”“an,” “the,” and “said” are intended to mean that there are one or moreof the elements. The terms “comprising,” “including,” and “having” areintended to be inclusive and mean that there may be additional elementsother than the listed elements.

The subject matter described herein can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structural means disclosed in this specification andstructural equivalents thereof, or in combinations of them. The subjectmatter described herein can be implemented as one or more computerprogram products, such as one or more computer programs tangiblyembodied in an information carrier (e.g., in a machine-readable storagedevice), or embodied in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers). A computerprogram (also known as a program, software, software application, orcode) can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program does not necessarily correspond to a file. A programcan be stored in a portion of a file that holds other programs or data,in a single file dedicated to the program in question, or in multiplecoordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions of the subject matter describedherein by operating on input data and generating output. The processesand logic flows can also be performed by, and apparatus of the subjectmatter described herein can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, (e.g., EPROM, EEPROM, and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having a display device, e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,(e.g., a mouse or a trackball), by which the user can provide input tothe computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The techniques described herein can be implemented using one or moremodules. As used herein, the term “module” refers to computing software,firmware, hardware, and/or various combinations thereof. At a minimum,however, modules are not to be interpreted as software that is notimplemented on hardware, firmware, or recorded on a non-transitoryprocessor readable recordable storage medium (i.e., modules are notsoftware per se). Indeed “module” is to be interpreted to always includeat least some physical, non-transitory hardware such as a part of aprocessor or computer. Two different modules can share the same physicalhardware (e.g., two different modules can use the same processor andnetwork interface). The modules described herein can be combined,integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module can be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules can beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules can be moved from onedevice and added to another device, and/or can be included in bothdevices.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web browser through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), e.g., the Internet.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about” and “substantially,” are not to be limited tothe precise value specified. In at least some instances, theapproximating language may correspond to the precision of an instrumentfor measuring the value. Here and throughout the specification andclaims, range limitations may be combined and/or interchanged, suchranges are identified and include all the sub-ranges contained thereinunless context or language indicates otherwise.

1-20. (canceled)
 21. A system, comprising: at least one inlineinspection tool; at least one sensor configured within and communicablycoupled to the inline inspection tool, wherein the at least one inlineinspection tool and the at least one sensor are configured to acquireinspection data related to one or more pipelines; and a cloud-basedcomputing system comprising a memory, a database, a communicationinterface and at least one data processor configured to executinginstructions stored in the memory causing the at least one dataprocessor to: receive the inspection data from the at least one inlineinspection tool; perform an analysis of the inspection data to generatea virtual structural model of the one or more pipelines based on theinspection data; and determine one or more physical states of the one ormore pipelines using the virtual structural model.
 22. The system ofclaim 21, wherein the inspection data includes an indication of at leastone of a volumetric wall loss, a pipeline orientation, a pipeline crack,a pressure of a fluid within a pipeline, a flow of a fluid within apipeline, a geometry of a pipeline, a centerline of a pipeline, a weldanomaly, a bending strain, a material property, and a pipeline fitting.23. The system of claim 21, wherein the inspection data includesmagnetic, ultrasonic, radiographic, and/or electromagnetic inspectiondata.
 24. The system of claim 21, wherein the inspection data isacquired as the inline inspection tool traverses an above-groundpipeline or a below-ground pipeline.
 25. The system of claim 21, whereinthe communication interface is a wired interface or wireless interface.26. The system of claim 21, wherein the database and/or the memory storethe inspection data including at least one of historical inspection dataof the one or more pipelines, service contact information related to theone or more pipelines, warranty information associated with the one ormore pipelines, and regulatory data associated with pipeline maintenanceand pipeline operation.
 27. The system of claim 21, wherein the analysisis a finite element analysis.
 28. The system of claim 27, wherein thefinite element analysis includes correlating inspection data associatedwith a particular location of the one or more pipelines to mapproperties of the one or more pipelines at the particular location ofthe one or more pipelines.
 29. The system of claim 21, wherein the atleast one data processor is further configured to determine the one ormore physical states as a historical physical state of the one or morepipelines, a current physical state of the one or more pipelines, or apredicted physical state of the one or more pipelines.
 30. The system ofclaim 29, wherein the at least one data processor is further configuredto generate an overlapping representation of the historical physicalstate of the one or more pipelines and the current physical state of theone or more pipelines.
 31. The system of claim 29, wherein responsive todetermining the current physical state of the one or more pipelines, theat least one data processor is further configured to predict when theone or more pipelines will cease to operate within an operationalthreshold boundary.
 32. The system of claim 21, wherein the at least onesensor includes a pressure sensor, a flow sensor, a vibration sensor, ora thermal sensor.
 33. The system of claim 21, wherein the system furtherincludes at least one sensor configured on the one or more pipelinesthat is external to the inline inspection tool.
 34. The system of claim21, wherein the at least one data processor is further configured togenerate reporting data as combined analysis data or standalone analysisreporting data.
 35. The system of claim 34, wherein the combinedanalysis reporting data includes predictive data associated with the oneor more pipelines based on two or more physical states determined usingthe virtual structural model.
 36. The system of claim 34, wherein thestandalone analysis reporting data includes data associated with asingle physical state determined using the virtual structural model, thesingle physical state selected by an operator or the system.
 37. Thesystem of claim 34, wherein the at least one data processor is furtherconfigured to provide the reporting data to personnel associated withthe one or more pipelines based on role-based security credentials. 38.The system of claim 34, wherein the at least one data processor isfurther configured to provide the reporting data as an input to anintegrity management plan associated with the one or more pipelines. 39.The system of claim 38, wherein the integrity management plan identifiesone of maximal throughput of the one or more pipelines, minimaloperational interruption of the one or more pipelines, or cost-basedoperational objectives of the one or more pipelines.
 40. The system of38, wherein the integrity management plan includes instructions for anoperator of the one or more pipelines to perform maintenance operationson portions of the one or more pipelines at a future date.